Service ticket analysis using an analytics device

ABSTRACT

A device may receive ticket information associated with one or more service tickets. The ticket information may include a ticket category and ticket data associated with the one or more service tickets. The ticket data may be associated with the ticket category. The ticket data may describe information related to resolving the one or more service tickets. The device may identify, based on the ticket category or the ticket data, an association between the ticket category and an analysis category. The analysis category may be used to analyze the ticket information. The device may generate categorized ticket information based on the analysis category and the ticket information. The categorized ticket information may include the ticket data, and the ticket data may be associated with the analysis category. The device may provide the categorized ticket information.

RELATED APPLICATION

This application claims priority under 35 U.S.C. §119 to IndianProvisional Patent Application No. 5713/CHE/2014, filed on Nov. 13,2014, the content of which is incorporated by reference herein in itsentirety.

BACKGROUND

An application provider (e.g., a business, an organization, or the like)may provide an application (e.g., an online application, a computerapplication, or the like). The organization may manage theimplementation and operation of the application, or may outsourcemanagement of the implementation and operation of the application to anapplication manager. The application manager may process variousformats, types, and amounts of data while implementing and operating theapplication.

SUMMARY

According to some possible implementations, a device may receive ticketinformation associated with one or more service tickets. The ticketinformation may include a ticket category and ticket data associatedwith the one or more service tickets. The ticket data may be associatedwith the ticket category. The ticket data may describe informationrelated to resolving the one or more service tickets. The device mayidentify, based on the ticket category or the ticket data, anassociation between the ticket category and an analysis category. Theanalysis category may be used to analyze the ticket information. Thedevice may generate categorized ticket information based on the analysiscategory and the ticket information. The categorized ticket informationmay include the ticket data, and the ticket data may be associated withthe analysis category. The device may provide the categorized ticketinformation.

According to some possible implementations, a computer-readable mediummay store instructions that, when executed by a processor, cause theprocessor to receive ticket information associated with one or moreservice tickets. The ticket information may include a ticket categoryand ticket data associated with the one or more service tickets. Theticket category may be associated with the ticket data, and the ticketdata may describe information related to resolving the one or moreservice tickets. The instructions may cause the processor to identify,based on the ticket category or the ticket data, an analysis categoryassociated with the ticket category. The instructions may cause theprocessor to generate categorized ticket information based on theanalysis category and the ticket information. The categorized ticketinformation may include the ticket data, and the ticket data may beassociated with the analysis category. The instructions may cause theprocessor to analyze the categorized ticket information to determineanalysis information. The instructions may cause the processor togenerate an analysis report that describes the analysis information, andto provide, for display, the analysis report.

According to some possible implementations, a method may includereceiving, by a device, ticket information associated with one or moreservice tickets. The ticket information may include a ticket categoryand ticket data associated with the one or more service tickets. Theticket data may be associated with the ticket category, and the ticketdata may describe information related to resolving the one or moreservice tickets. The method may include identifying, by the device andbased on the ticket category or the ticket data, an association betweenthe ticket category and an analysis category. The analysis category maybe used to analyze the ticket information. The method may includegenerating, by the device, categorized ticket information based on theanalysis category and the ticket information. The categorized ticketinformation may include the ticket data, and the ticket data may beassociated with the analysis category. The method may includedetermining, by the device and based on the ticket data, an outlier ofthe categorized ticket information. The method may include providing, bythe device, an outlier report that includes information identifying theoutlier. The outlier report may request an outlier action indicator thatindicates an action for the device to perform related to the outlier.The method may include selectively removing or including, by the deviceand based on the action indicated by the outlier action indicator, theoutlier with the categorized ticket information. The method may includeproviding, by the device, the categorized ticket information.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are diagrams of an overview of an example implementationdescribed herein;

FIG. 2 is a diagram of an example environment in which systems and/ormethods, described herein, may be implemented;

FIG. 3 is a diagram of example components of one or more devices of FIG.2;

FIG. 4 is a flow chart of an example process for categorizing ticketinformation and processing outliers;

FIGS. 5A-5E are diagrams of an example implementation relating to theexample process shown in FIG. 4;

FIGS. 6A-6C are diagrams of another example implementation relating tothe example process shown in FIG. 4;

FIG. 7 is a flow chart of an example process for analyzing ticketinformation and generating an analysis report; and

FIGS. 8A-8D are diagrams of an example implementation relating to theexample process shown in FIG. 7.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

An application provider may provide an application via a user device, aserver device, or the like. The application provider may manage theimplementation and/or operation of the application (e.g., by employinginformation technology specialists), or may outsource the implementationand/or operation of the application (e.g., by entering into anapplication outsourcing (AO) contract with an application manager). Theapplication manager may manage the implementation and/or operation ofthe application using a service management tool.

The application provider may provide, to the application manager andusing the service management tool, a service ticket that describes anissue related to the application (e.g., a service delay, a serviceoutage, or the like). Based on the service ticket, the applicationmanager may resolve the issue. Different issues may be associated withdifferent levels of urgency (e.g., an outage that affects all users of aparticular application may be associated with a higher level of urgencythan an outage that affects a portion of users). The applicationprovider and the application manager may agree on a level of service forissues associated with different levels of urgency (e.g., the level ofservice may prescribe a target time to respond to and/or resolve aservice ticket associated with a particular level of urgency, or thelike). A priority level of the service ticket, associated with the levelof urgency of the issue, may be indicated by the service ticket (e.g.,by a priority indicator associated with the service ticket, or thelike).

The application manager may wish to analyze ticket information (e.g.,may wish to determine an average response time to a particular prioritylevel of service ticket, an average response time to service ticketsoriginating from a particular geographic area, an average response timeto service tickets from a particular industry, or the like) to assessperformance in responding to and/or resolving service tickets. However,the application manager may manage a variety of applications thatprovide service tickets via a variety of service management tools. Forexample, a first application provider may provide service tickets thatinclude information in a different format than a second applicationprovider. Further, the application manager may not use a unifiedplatform to analyze a variety of service tickets from differentapplication providers and in different formats.

Implementations described herein may assist the application manager ingathering and analyzing ticket information. The application manager mayprovide, to an analytics device, a batch of ticket information (e.g., ina spreadsheet, or the like). The batch of ticket information may includea variety of ticket data (e.g., a ticket number, a ticket priority, adate and time a service ticket was received, a date and time a serviceticket was resolved, a status of a service ticket, or the like) or auser-defined category of ticket data (e.g., a category of ticket dataassociated with a particular client, application, and/or project thatthe user wants to analyze). The analytics device may categorize theticket information to create categorized ticket information.

The analytics device may provide the categorized ticket information to aserver device (e.g., a server device that stores other categorizedticket information previously provided by the analytics device oranother device). The analytics device may analyze the categorized ticketinformation stored by the server device to generate an analysis report(e.g., the analytics device may determine and/or remove outliers, maygroup tickets by a particular metric and/or a stratification parameter,may produce a graphical representation of the categorized ticketinformation, or the like). The analytics device may provide thecategorized ticket information and the analysis report to theapplication manager or another party, such as the application provider.The application manager or other party may interact with the analysisreport to cause additional analysis of the categorized ticketinformation to be provided (e.g., the analytics device may provideadditional information, may determine and provide different information,or the like).

In this way, the analytics device may categorize ticket information ofdifferent data formats and origins, and may provide an interactiveanalysis report to the application manager based on categorized ticketinformation. This may aid the application manager in storing andanalyzing categorized ticket information using a single device, ratherthan requiring multiple, different devices to analyze different typesand formats of ticket information.

FIGS. 1A and 1B are diagrams of an overview of an example implementation100 described herein. For the purpose of FIGS. 1A and 1B, assume that auser (e.g., an application manager) compiles a batch of ticketinformation including a ticket number (shown as Ticket #) and a metricrelated to service tickets.

As shown in FIG. 1A, a user device may provide a batch of ticketinformation to an analytics device. As further shown, the analyticsdevice may categorize the batch of ticket information to createcategorized ticket information. The analytics device may furthergenerate a preliminary report and may discard one or more outliers ofthe categorized ticket information. In some implementations, theanalytics device may not discard the one or more outliers (e.g., theanalytics device may store the one or more outliers locally, may providethe one or more outliers to a server device, or the like). As furthershown, the analytics device may provide a preliminary report to the userdevice. The preliminary report may include information that describesthe one or more outliers. As further shown, the user device maytransmit, to the analytics device, a request to upload and analyze thecategorized ticket information. Assume that the analytics deviceanalyzes the categorized ticket information.

As shown in FIG. 1B, the analytics device may generate an analysisreport based on analyzing the categorized ticket information. In someimplementations, the analytics device may provide the analysis report toanother device, such as the user device. As shown, the analysis reportmay include graphical information that describes the categorized ticketinformation. As further shown, the analysis report may facilitate a userinteraction to generate further analysis information (e.g., by a userinteraction with a “Show more charts based on data” button, a userinteraction with an element of the graphical information, or the like).As shown, the analytics device may provide the categorized ticketinformation to a server device for storage.

In this way, the analytics device may receive ticket information from auser device. The analytics device may process the ticket information bydetermining one or more outliers and by categorizing the ticketinformation, and may generate and/or provide an interactive analysisreport based on the categorized ticket information. By interacting withthe analysis report, a user may cause the analytics device to performadditional analysis and/or provide different information. In this way,the analytics device may aid the user in understanding ticketinformation, which may improve the user's management of the application.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods, described herein, may be implemented. As shown in FIG.2, environment 200 may include a user device 210, a server device 220,an analytics device 230, and a network 240. Devices of environment 200may interconnect via wired connections, wireless connections, or acombination of wired and wireless connections.

User device 210 may include one or more devices capable of receiving,generating, storing, processing, and/or providing ticket information.For example, user device 210 may include a communication and/orcomputing device, such as a laptop computer, a tablet computer, ahandheld computer, or a similar type of device. In some implementations,user device 210 may allow a user to access, view, and/or interact withanalytics device 230 (e.g., via a user interface) in order to format,approve, preview, upload, etc. ticket information, in order to generate,access, view, or interact with an analysis report, or the like.

Server device 220 may include one or more devices capable of receiving,generating, storing, processing, and/or providing information. Forexample, server device 220 may include a server or a similar device. Insome implementations, server device 220 may store and/or access ticketinformation (e.g., captured using a service management tool) to beprovided to analytics device 230. In some implementations, server device220 may analyze stored ticket information to generate analysisinformation, and may provide the analysis information to analyticsdevice 230 or another device.

Analytics device 230 may include one or more devices capable ofreceiving, generating, storing, processing, and/or providing informationassociated with analyzing ticket information. For example, analyticsdevice 230 may include a server device or a collection of serverdevices. In some implementations, analytics device 230 may analyzeticket information. Additionally, or alternatively, analytics device 230may generate and/or provide an analysis report associated with analyzingticket information. In some implementations, analytics device 230 maydetermine mapping information, and may use the mapping information togenerate categorized ticket information based on ticket information.

Network 240 may include one or more wired and/or wireless networks. Forexample, network 240 may include a cellular network (e.g., a long-termevolution (LTE) network, a 3G network, a code division multiple access(CDMA) network, etc.), a public land mobile network (PLMN), a local areanetwork (LAN), a wide area network (WAN), a metropolitan area network(MAN), a telephone network (e.g., the Public Switched Telephone Network(PSTN)), a private network, an ad hoc network, an intranet, theInternet, a fiber optic-based network, a cloud computing network, or thelike, and/or a combination of these or other types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 may beimplemented within a single device, or a single device shown in FIG. 2may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 200 may perform one or more functions described as beingperformed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to user device 210, server device 220, and/or analyticsdevice 230. In some implementations, user device 210, server device 220,and/or analytics device 230 may include one or more devices 300 and/orone or more components of device 300. As shown in FIG. 3, device 300 mayinclude a bus 310, a processor 320, a memory 330, a storage component340, an input component 350, an output component 360, and acommunication interface 370.

Bus 310 may include a component that permits communication among thecomponents of device 300. Processor 320 is implemented in hardware,firmware, or a combination of hardware and software. Processor 320 mayinclude a processor (e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), an accelerated processing unit (APU), etc.), amicroprocessor, and/or any processing component (e.g., afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), etc.) that interprets and/or executes instructions.Memory 330 may include a random access memory (RAM), a read only memory(ROM), and/or another type of dynamic or static storage device (e.g., aflash memory, a magnetic memory, an optical memory, etc.) that storesinformation and/or instructions for use by processor 320.

Storage component 340 may store information and/or software related tothe operation and use of device 300. For example, storage component 340may include a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, a solid state disk, etc.), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of computer-readable medium, along with acorresponding drive.

Input component 350 may include a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, amicrophone, etc.). Additionally, or alternatively, input component 350may include a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, an actuator,etc.). Output component 360 may include a component that provides outputinformation from device 300 (e.g., a display, a speaker, one or morelight-emitting diodes (LEDs), etc.).

Communication interface 370 may include a transceiver-like component(e.g., a transceiver, a separate receiver and transmitter, etc.) thatenables device 300 to communicate with other devices, such as via awired connection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 may permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface, orthe like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes in response to processor 320 executingsoftware instructions stored by a computer-readable medium, such asmemory 330 and/or storage component 340. A computer-readable medium isdefined herein as a non-transitory memory device. A memory deviceincludes memory space within a single physical storage device or memoryspace spread across multiple physical storage devices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for categorizing ticketinformation and processing outliers. In some implementations, one ormore process blocks of FIG. 4 may be performed by analytics device 230.In some implementations, one or more process blocks of FIG. 4 may beperformed by another device or a set of devices separate from orincluding analytics device 230, such as user device 210 and/or serverdevice 220.

As shown in FIG. 4, process 400 may include receiving ticket information(block 410). For example, analytics device 230 may receive ticketinformation (e.g., from user device 210 or another device). The ticketinformation may include information related to service tickets. In someimplementations, the ticket information may be provided by user device210 (e.g., the ticket information may be uploaded, emailed, etc. toanalytics device 230 by user device 210). Additionally, oralternatively, the ticket information may be provided by another device(e.g., server device 220). In some implementations, the ticketinformation may be stored in a data structure (e.g., a spreadsheet, orthe like). In some implementations, the ticket information may includeinformation related to multiple, different users, application, and/orprojects.

In some implementations, the ticket information may include informationidentifying a user, an application, and/or a project associated with theticket information (e.g., a client identifier that identifies aparticular client or user, a project identifier that identifies aparticular project, a start date of the particular project, a start timeof a shift, an end time of the shift, a number of days in a week thatthe shift occurs, a geographical location identifier, an operating groupidentifier, a team size identifier, a technology identifier thatidentifies a technology associated with the user or the application, orthe like). In some implementations, the ticket information may include aproject profile identifier (e.g., information identifying a particularproject to which the ticket information is related) corresponding to aproject profile stored by analytics device 230. In some implementations,the ticket information may include a client profile identifier (e.g.,information identifying a particular client to which the ticketinformation is related), corresponding to a client profile stored byanalytics device 230.

In some implementations, the ticket information may include ticket data.The ticket data may include resolution information related to resolvinga ticket, such as a ticket resolution status, a priority category (e.g.,a category for a priority identifier that identifies a priority level ofa ticket), a date of receipt of the ticket, a date of resolution of theticket, or the like. In some implementations, the ticket data may beprovided in association with a user-defined category (e.g., the ticketdata and the user-defined category may be provided as part of a singlefile).

In some implementations, the ticket information may be associated with auser-defined category. The user-defined category may include informationrelated to analyzing ticket data, such as a project name category, anapplication name category, a line of business category, an “effort inminutes” category (e.g., a category for ticket information thatdescribes an amount of time spent evaluating, processing, and/orresolving a ticket), a last modified date category (e.g., a category forticket information that describes a date on which the ticket informationwas last modified), a ticket type category (e.g., an incident tickettype, a problem ticket type, a work request ticket type, etc.), or thelike. A user may define a user-defined category to be associated withticket information that is unique to a particular project, application,and/or client, and may provide the user-defined category and/or theticket information associated with the user-defined category toanalytics device 230 for analysis. In this way, analytics device 230 mayfacilitate storage, categorization, and analysis of ticket informationthat is unique to a particular project, application, and/or client.

In some implementations, the ticket information may include informationrelated to a service level agreement (SLA). A service level agreementmay be an agreement, between an application outsourcing (AO) serviceprovider and a client, that defines a service level for providing an AOservice. For example, an AO service provider may agree with a client toprovide a first service level in association with a first priority levelof ticket, a second service level in association with a second prioritylevel of ticket, and so on. The ticket information may define an SLAresponse category (e.g., that includes information related to whetherthe AO service provider achieved the agreed-upon service level inassociation with a response to a ticket), an SLA resolution category(e.g., that includes information related to whether the AO serviceprovider achieved the agreed-upon service level in association with aresolution of a ticket), or another category related to an SLA. In someimplementations, the information related to the SLA may be optional(e.g., analytics device 230 may not require the SLA ticket informationto perform the AO analysis).

In some implementations, the ticket information may include effort data.Effort data may include information describing an average effort inresolving a batch of service tickets (e.g., an average time spentresolving a batch of service tickets, or the like). In someimplementations, the effort data may be determined based on an effortper individual service ticket. Additionally, or alternatively, theeffort data may be determined based on an aggregation (e.g., averageeffort data for a batch of service tickets, or the like). In someimplementations, the effort data may be defined in terms of a full-timeequivalent (FTE) unit (e.g., a unit of time equivalent to a time inputby a full-time worker).

In some implementations, the ticket information may include informationrelated to an open service ticket (e.g., an unresolved service ticket).For example, analytics device 230 may receive ticket information thatdescribes one or more service tickets that have not been resolved (e.g.,the one or more service tickets may not be associated with resolutioninformation, effort data, a date of resolution, or the like). In someimplementations, analytics device 230 may ensure that the ticketinformation of the one or more service tickets does not include theresolution information, the effort data, or the date of resolution.

In some implementations, the ticket information may be provided via auser interface (e.g., based on a user interaction with a drop down menu,based on a user entering information in an input field, etc.). Forexample, assume that analytics device 230 receives a batch of ticketinformation related to a particular project called “Project1.” Assumefurther that analytics device 230 has previously received other ticketinformation related to Project1. Rather than requiring the ticketinformation to include information identifying Project1, analyticsdevice 230 may provide a user interface feature allowing a user toidentify a project profile of Project1 (e.g., a drop down menu thatincludes a “Project1” option, based on analytics device 230 havingpreviously created a project profile associated with Project1). In thisway, analytics device 230 may receive ticket information via a userinterface.

In some implementations, analytics device 230 may provide an input fieldin association with a user interface based on not receiving particularticket information. For example, assume that analytics device 230requires particular ticket information in order to analyze a batch ofticket information. Assume further that the particular ticketinformation is not included with the batch of ticket information. Inthat case, analytics device 230 may determine that the particular ticketinformation is not included with the batch of ticket information.Analytics device 230 may further provide, for display in associationwith a user interface, an input field that facilitates input of theparticular ticket information. In this way, analytics device 230 mayensure that particular ticket information is provided, in order toproperly analyze the batch of ticket information.

As further shown in FIG. 4, process 400 may include determining mappinginformation that associates the ticket information with an analysiscategory (block 420). For example, analytics device 230 may determinemapping information that associates the ticket information with ananalysis category. In some implementations, analytics device 230 maydetermine the mapping information without user input (e.g., analyticsdevice 230 may automatically determine the mapping information, ratherthan receiving user input that identifies the mapping information).Additionally, or alternatively, analytics device 230 may receive themapping information (e.g., based on user input, from user device 210,from server device 220, or the like).

In some implementations, the mapping information may be associated withrequired ticket information. For example, assume that analytics device230 requires particular ticket information, associated with a particularanalysis category, to analyze the ticket information (e.g., analyticsdevice 230 may require a ticket resolution status, a priority level of aservice ticket, a date of receipt of a service ticket, a date ofresponse to a service ticket, or the like, to perform the analysis). Inthis case, the mapping information may specify that the particularanalysis category is required. Analytics device 230 may only accept abatch of ticket information that includes ticket data associated withthe particular analysis category based on the mapping information. Insome implementations, analytics device 230 may provide, for display, anindication that the particular analysis category is required (e.g., an“x” next to a name of the particular analysis category, or the like).Upon receiving the particular ticket information associated with theparticular analysis category, analytics device 230 may provide, fordisplay, an indication that analytics device 230 received the particularticket information (e.g., a check mark next to the name of theparticular analysis category, or the like). In this way, analyticsdevice 230 may ensure that required ticket information is provided byuser device 210.

In some implementations, the mapping information may be associated withoptional ticket information. For example, assume that optional ticketinformation is not required by analytics device 230 to analyze a batchof ticket information, but may be used by analytics device 230 toperform additional analysis (e.g., the optional ticket information maybe associated with a user-defined category, or the like). Analyticsdevice 230 may define an optional analysis category for the optionalticket information (e.g., based on an input received from user device210, or the like). Mapping information may associate the optional ticketinformation and the optional analysis category. Upon receiving theoptional ticket information, analytics device 230 may perform theadditional analysis (e.g., based on a user interaction, or the like). Inthis way, analytics device 230 may provide additional analysis based onoptional ticket information and/or a user-defined category of ticketinformation.

In some implementations, an analysis category may match a ticketinformation category. For example, assume that particular ticketinformation is associated with a ticket information category named“Shift Start Time.” Assume further that an analysis category is named“Shift Start Time.” Based on determining that the name of the ticketinformation category matches the name of the analysis category,analytics device 230 may automatically determine mapping informationthat associates the ticket information category and the analysiscategory (e.g., without user input).

In some implementations, analytics device 230 may fail to automaticallydetermine mapping information. For example, assume that analytics device230 fails to determine mapping information (e.g., fails to automaticallymatch a ticket information category to an analysis category). In thatcase, analytics device 230 may provide an indication of the failure todetermine the mapping information. Analytics device 230 may furtherreceive the mapping information from another device (e.g., from userdevice 210, based on the indication of the failure to determine themapping information). In some implementations, the mapping informationmay be received via a user interface (e.g., a user may indicate anassociation between an analysis category and a ticket informationcategory via the user interface).

As further shown in FIG. 4, process 400 may include categorizing theticket information based on the analysis category to create categorizedticket information (block 430). For example, analytics device 230 maycategorize the ticket information, based on the analysis category andthe mapping information, to create categorized ticket information. Insome implementations, analytics device 230 may rearrange the categorizedticket information (e.g., may group related categorized ticketinformation, or the like). In this way, analytics device 230 may provideticket information to server device 220 in a standardized and moreeasily analyzed format, thus improving performance of server device 220and analytics device 230.

As further shown in FIG. 4, process 400 may include determining anoutlier of the ticket information (block 440). For example, analyticsdevice 230 may determine an outlier of the categorized ticketinformation. In some implementations, analytics device 230 may determinethe outlier based on a statistical analysis (e.g., based on a standarddeviation, or the like). In some implementations, analytics device 230may determine the outlier based on a user input (e.g., a user mayspecify, via user device 210, a threshold value to define an outlier, arange of values to define the outlier, etc.).

An outlier may include ticket information that varies (e.g., by aparticular threshold) from a particular metric of the ticketinformation. For example, assume that ten service tickets are eachassociated with a turnaround time between fifty hours and one hundredhours. Assume further that an eleventh service ticket is associated witha turnaround time of six hundred hours. Analytics device 230 maydetermine that the eleventh service ticket is an outlier based on theturnaround time associated with the eleventh service ticket beingoutside of the range of the other ten service tickets. In someimplementations, analytics device 230 may determine multiple, differentoutliers of a batch of ticket information. In some implementations, anoutlier may be caused by a process variation (e.g., an uncontrollablevariable in a process that may cause the particular metric of the ticketinformation to vary, or the like), a client action (e.g., a clientfailing to submit a service ticket for a period of time after generatingthe service ticket), a natural cause (e.g., a natural disaster, or thelike, that delays resolution of a service ticket), or the like.

In some implementations, analytics device 230 may determine an outlierbased on a statistical analysis. For example, analytics device 230 maydetermine a statistical measure of a batch of ticket information (e.g.,a standard deviation of the batch of ticket information, a variance ofthe batch of ticket information, or the like). Analytics device 230 maydetermine one or more outliers based on the statistical measure (e.g.,if a particular ticket of the batch of ticket information is not withinthe standard deviation, analytics device 230 may determine theparticular ticket to be an outlier). In this way, analytics device 230may determine an outlier of a batch of ticket information withoutreceiving a definition of the outlier.

In some implementations, analytics device 230 may categorize an outlier.For example, assume that analytics device 230 determined an outlierbased on a geographical location (e.g., a particular service ticket,associated with a geographical location that is different than thegeographical locations that are associated with other service tickets,was determined to be an outlier). Analytics device 230 may categorizethe outlier based on the geographical location. As another example,assume that analytics device 230 determines an outlier based on a timeof resolution (e.g., another particular service ticket, associated witha time of resolution that is different than the times of resolution thatare associated with other service tickets, was determined to be anoutlier). Analytics device 230 may categorize the outlier based on thetime of resolution. In this way, analytics device 230 may determine,categorize, and store outliers, which may aid the user in analyzing theoutliers (e.g., to determine a cause of the outliers, or the like).

In some implementations, analytics device 230 may determine an outlierbased on a user input. For example, assume that a user wants to defineas an outlier any service ticket associated with a turnaround timegreater than three hundred hours. User device 210 may provide a rule, toanalytics device 230, that analytics device 230 is to determine that anyservice ticket associated with a turnaround time greater than threehundred hours is an outlier. Based on the rule, analytics device 230 maydetermine that one or more service tickets associated with turnaroundtimes greater than three hundred hours are outliers, and may process theone or more service tickets accordingly. In this way, analytics device230 may determine one or more outliers based on a rule provided by auser, and thus may more effectively analyze the ticket information basedon a preference of the user.

As further shown in FIG. 4, process 400 may include providing apreliminary report that includes information that identifies the outlier(block 450). For example, analytics device 230 may prepare and/orprovide a preliminary report that describes the ticket information. Thepreliminary report may include information that identifies the outlier.In some implementations, analytics device 230 may provide thepreliminary report to user device 210 (e.g., for display via a userinterface of user device 210).

In some implementations, the preliminary report may provide a preview ofan upload (e.g., based on a user interaction, automatically, or thelike). For example, assume that user device 210 provides a batch ofticket information to analytics device 230 to upload to server device220. Assume further that analytics device 230 has created categorizedticket information. In some implementations, before uploading thecategorized ticket information to server device 220, analytics device230 may provide a preview of the categorized ticket information to beuploaded. In this way, analytics device 230 may ensure that a user iscapable of previewing the categorized ticket information beforeproviding the categorized ticket information to server device 220 (e.g.,to ensure that the categorized ticket information is accurate).

The preview of the categorized ticket information may includeinformation related to the categorized ticket information (e.g., anincident number, a priority indicator, a ticket status indicator, anindicator of a reported date, an indicator of a resolved date, anindicator of a responded date, an SLA resolution status, an SLA responsestatus, or the like). In some implementations, analytics device 230 mayprovide the preview of the categorized ticket information automatically(e.g., analytics device 230 may always provide the preview of thecategorized ticket information before uploading the categorized ticketinformation to server device 220). Additionally, or alternatively,analytics device 230 may provide the preview of the categorized ticketinformation based on a user interaction (e.g., a user may request, via auser interface of user device 210, that analytics device 230 provide thepreview of the categorized ticket information).

In some implementations, the preliminary report may include a graphicalrepresentation of the categorized ticket information. For example, thepreliminary report may provide a user interface (e.g., for display viauser device 210). The user interface may facilitate a user interactionto request a graphical representation of the categorized ticketinformation. Based on a user interaction with the graphical interface,analytics device 230 may provide a graphical representation of thecategorized ticket information. In this way, analytics device 230 maypresent categorized ticket information in a more easily understood andaccessible format, by providing a graphical representation of thecategorized ticket information.

In some implementations, the graphical representation of the categorizedticket information may include an outlier. For example, assume thatanalytics device 230 has determined an outlier of a batch of categorizedticket information. Assume further that analytics device 230 receives anindicator to include the outlier in the graphical representation of thebatch of categorized ticket information (e.g., via an “include outlier”check box of the user interface, or the like). Based on receiving theindicator to include the outlier, analytics device 230 may include theoutlier in the graphical representation of the categorized ticketinformation. In this way, analytics device 230 may provide a graphicalrepresentation of categorized ticket information that includes anoutlier, to aid a user in understanding the relationship of thecategorized ticket information to the outlier (e.g., a relativemagnitude of the outlier compared to the categorized ticket information,or the like).

Additionally, or alternatively, analytics device 230 may not include anoutlier in the graphical representation of the categorized ticketinformation. For example, assume that analytics device 230 receives anindication not to include an outlier in the graphical representation ofthe categorized ticket information. Based on receiving the indicationnot to include the outlier, analytics device 230 may prevent the outlierfrom being included in the graphical representation of the categorizedticket information. In this way, analytics device 230 may provide agraphical representation of categorized ticket information not includingan outlier, which may aid a user in understanding, processing, and/orvisualizing the categorized ticket information.

In some implementations, the preliminary report may include informationthat identifies an outlier. For example, after determining an outlier ofa batch of ticket information, analytics device 230 may includeinformation that identifies the outlier in the preliminary report. Thepreliminary report may indicate one or more actions that analyticsdevice 230 may take related to the outlier (e.g., the preliminary reportmay define an outlier action indicator, which is described in moredetail elsewhere herein). In some implementations, the informationidentifying the outlier may identify a reason for the outlier. Forexample, assume that analytics device 230 determines that a particularservice ticket is an outlier based on the service ticket beingassociated with a resolution time that is higher than other servicetickets of a similar priority level. Analytics device 230 may provide,with the preliminary report, information that identifies the outlier anda reason that the particular outlier was determined. In this way,analytics device 230 may provide information identifying outliers andreasons the outliers were determined, to assist a user in evaluating theticket information.

As further shown in FIG. 4, process 400 may include receiving an outlieraction indicator that indicates an action to perform related to theoutlier (block 460). For example, analytics device 230 may receive, fromuser device 210, an outlier action indicator. The outlier actionindicator may indicate an action to perform related to the outlier(e.g., remove the outlier from the ticket information, include theoutlier with the ticket information, or the like). In someimplementations, analytics device 230 may receive the outlier actionindicator based on a user interaction (e.g., a user interaction with auser interface provided by user device 210).

In some implementations, the outlier action indicator may causeanalytics device 230 to remove the outlier from the ticket information.For example, assume that a user wants to remove outliers from a batch ofticket information before analytics device 230 provides the ticketinformation to server device 220. Based on the information identifyingthe outlier in the preliminary report, user device 210 may provide anoutlier action indicator to analytics device 230 (e.g., based on a userinteraction with a user interface) that indicates that analytics device230 is to remove the outlier. Additionally, or alternatively, theoutlier action indicator may indicate that analytics device 230 is toremove all outliers from the ticket information. In this way, a user maycause analytics device 230 to remove one or more outliers from a batchof ticket information.

In some implementations, the outlier action indicator may causeanalytics device 230 to include the outlier with the ticket information.For example, assume that a user of user device 210 determines thatanalytics device 230 incorrectly flagged an outlier (e.g., by reviewingthe preliminary report). User device 210 may provide an outlier actionindicator that causes analytics device 230 to include the outlier whenproviding the ticket information to server device 220. In this way,analytics device 230 may include an incorrectly determined outlier witha batch of ticket information, based on an outlier action indicator.

In some implementations, the outlier action indicator may causeanalytics device 230 to include all outliers with the categorized ticketinformation. For example, assume that a user wants to include alloutliers with a batch of ticket information for analytics device 230 toprovide to server device 220. User device 210 may provide an outlieraction indicator to analytics device 230 (e.g., based on a userinteraction with a user interface) that indicates that analytics device230 is to include all outliers with the batch of ticket information. Inthis way, a user may cause analytics device 230 to include all outlierswith a batch of ticket information (e.g., to facilitate analysis of theoutliers, or the like).

As further shown in FIG. 4, process 400 may include performing theaction indicated by the outlier action indicator (block 470). Forexample, analytics device 230 may perform the action indicated by theoutlier action indicator. In some implementations, analytics device 230may remove one or more outliers from a batch of ticket information(e.g., if the outlier action indicator indicated that analytics device230 is to remove a particular outlier, a group of outliers, alloutliers, or the like). Additionally, or alternatively, analytics device230 may include one or more outliers with a batch of ticket information(e.g., if the outlier action indicator indicated that analytics device230 is to include a particular outlier, a group of outliers, alloutliers, or the like).

In some implementations, analytics device 230 may include one or moreoutliers, and remove one or more other outliers. For example, assumethat analytics device 230 receives a first outlier action indicator thatindicates that analytics device 230 is to remove a first outlier from abatch of ticket information. Assume further that analytics device 230receives a second outlier action indicator that indicates that analyticsdevice 230 is to include a second outlier with the batch of ticketinformation. Based on the first and second outlier action indicators,analytics device 230 may remove the first outlier from the batch ofticket information, and may include the second outlier with the batch ofticket information. In this way, analytics device 230 may take differentactions with respect to different outliers, based on one or more outlieraction indicators.

As further shown in FIG. 4, process 400 may include providing thecategorized ticket information to a server device (block 480). Forexample, analytics device 230 may provide the categorized ticketinformation to server device 220 (e.g., by uploading the categorizedticket information to server device 220, or the like). In someimplementations, analytics device 230 may provide the categorized ticketinformation to server device 220 based on a user interaction (e.g., auser interaction with a user interface of user device 210).

In some implementations, analytics device 230 may provide thecategorized ticket information based on an interaction with thepreliminary report. For example, analytics device 230 may facilitate auser interaction with the preliminary report (e.g., via an “upload”button of a user interface displaying the preliminary report) to causeanalytics device 230 to receive an upload indicator (e.g., thatindicates that analytics device 230 is to provide the categorized ticketinformation to server device 220). Based on receiving the uploadindicator, analytics device 230 may provide the categorized ticketinformation to server device 220 and/or another device.

In some implementations, analytics device 230 may not provide thecategorized ticket information to server device 220. For example,analytics device 230 may facilitate a user interaction with thepreliminary report (e.g., via a “cancel upload” button of a userinterface displaying the preliminary report) to cause analytics device230 not to provide the categorized ticket information. Based onreceiving the user interaction, analytics device 230 may not upload thecategorized ticket information to server device 220. In this way,analytics device 230 may permit a user to cancel an upload ofcategorized ticket information.

In some implementations, analytics device 230 may iteratively and/orcontinuously provide categorized ticket information to server device220. For example, assume that analytics device 230 continuously receivesticket information from a device (e.g., user device 210). Assume furtherthat analytics device 230 continuously determines categorized ticketinformation, based on the ticket information and mapping information.Analytics device 230 may continuously provide the categorized ticketinformation to server device 220 (e.g., without user interaction). Inthis way, analytics device 230 may be configured to continuouslycategorize and provide ticket information to server device 220, whichmay improve an efficiency of server device 220 and/or analytics device230.

In some implementations, analytics device 230 may provide categorizedticket information from multiple, different projects to server device220. For example, assume that analytics device 230 receives ticketinformation from a first project and from a second project. Analyticsdevice 230 may categorize the ticket information from the first projectand the second project to create first categorized ticket informationand second categorized ticket information. In some implementations,analytics device 230 may provide the first categorized ticketinformation in association with the second categorized ticketinformation (e.g., as part of a single file, a single upload, or thelike). In this way, analytics device 230 may more efficiently providecategorized ticket information to server device 230.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIGS. 5A-5E are diagrams of an example implementation 500 relating toexample process 400 shown in FIG. 4. FIGS. 5A-5E show an example ofcategorizing ticket information and processing outliers. For the purposeof FIGS. 5A-5E, assume that a user interacts with a user interface ofuser device 210 that is provided by analytics device 230. Assume furtherthat analytics device 230 is receiving ticket information for aparticular client (shown as “Acme”), related to a particular project(shown as “project01”).

As shown in FIG. 5A, and by reference number 501, analytics device 230may provide a project demographics user interface, via which a user mayprovide information related to project demographics of the ticketinformation. As shown by reference number 502, the user may provide aclient name (e.g., Acme), a project identifier (e.g., project01), and aproject start date (e.g., Apr. 15, 2012). Here, the user has notselected a “create new” checkbox. If the user had selected the “createnew” checkbox, analytics device 230 may create a new client profileand/or project profile with which to associate ticket information. Insome implementations, analytics device 230 may receive ticketinformation related to multiple, different projects. In that case,analytics device 230 may require user device 210 to provide a projectidentifier. Additionally, or alternatively, analytics device 230 mayreceive ticket information related to a single project. In that case,analytics device 230 may not require user device 210 to provide aproject identifier.

As shown by reference number 503, analytics device 230 may receiveinformation describing a working shift (e.g., analytics device 230 hasreceived a shift start time of 08:30, a shift end time of 18:30, and anumber of five working days per week of the working shift). Theinformation describing the working shift may be used to determine and/orprovide effort data (e.g., in FTE units).

As shown by reference number 504, analytics device 230 may receiveinformation related to additional key parameters. Here, analytics device230 receives a geography identifier, identifying the Asia-Pacificgeographical region or origin that is associated with the ticketinformation of “APAC;” a technology identifier, identifying a technologyrelated to the ticket information of “.net;” and a work type identifier,identifying a work type related to the ticket information of“infrastructure outsourcing.”

As shown by reference number 505, analytics device 230 may receiveinformation related to a service level agreement (SLA). For example, auser may provide information identifying a response time SLA, aresolution time SLA, a time taken to resolve SLA (e.g., a length of timeto resolve a particular priority level of ticket), a time taken torespond SLA (e.g., a length of time to respond to a particular prioritylevel of ticket), or the like. As shown, assume a user interaction withan element of the user interface (e.g., a “+” button) causes analyticsdevice 230 to provide one or more input fields for the informationrelated to the SLA. As further shown, the information related to the SLAmay include a variety of priority levels of service tickets. In someimplementations, the information related to the SLA may be optional(e.g., analytics device 230 may not require the user to inputinformation related to the SLA).

As shown in FIG. 5B, analytics device 230 may provide a data converteruser interface, via which a user may provide information related tocategorizing ticket information. As shown by reference number 506,analytics device 230 may receive information identifying a data source(e.g., user device 210, or another device) and a source file (e.g., afile name of a batch of ticket information). As further shown, analyticsdevice 230 may receive information identifying a ticket categoryassociated with the ticket information (e.g., an incident category, aproblem category, a work request category, or the like). As shown,assume that analytics device 230 automatically (e.g., without userinput) populates one or more input fields based on a prior userinteraction (e.g., the “client” input field is populated by “Acme” basedon the user previously providing “Acme” in the “client” input field ofthe project demographics user interface described in association withFIG. 5A).

As shown by reference number 507, analytics device 230 may receiveeffort data (e.g., via one or more input fields provided in associationwith an “effort data capture” portion of the user interface). As furthershown, analytics device 230 may receive effort data in association witha variety of priority levels of service tickets. In someimplementations, the effort data may be included with the batch ofticket information (e.g., as ticket data, or the like). In that case,analytics device 230 may not provide the one or more input fields inassociation with the “effort data capture” portion of the userinterface. Additionally, or alternatively, analytics device 230 mayprovide the one or more input fields, and may use effort data receivedvia the one or more input fields instead of the effort data includedwith the batch of ticket information (e.g., the effort data input viathe one or more input fields may override the effort data included withthe batch of ticket information).

As shown in FIG. 5C, and by reference number 508, analytics device 230may receive mapping information related to a ticket information category(e.g., via a “field mapping” portion of the user interface). As shown byreference number 509, analytics device 230 may provide a list of one ormore ticket information categories (e.g., shown as “Source Fields,” andincluding ProjectName, ApplicationName, LOB, and so on). In someimplementations, the one or more ticket information categories mayinclude a user-defined category. As further shown, analytics device 230may provide for display a list of one or more analysis categories (e.g.,shown as “Destination Fields,” and including ApplicationName, Effort,and LOB Name (optional)).

As shown by reference number 510, assume that analytics device 230receives mapping information that associates a particular ticketinformation category and a particular analysis category (e.g., based ona user interaction, such as clicking the particular ticket informationcategory in the “Source Fields” list and clicking the particularanalysis category in the “Destination Fields” list). As further shown,assume that analytics device 230 receives a request to categorize theticket information (e.g., based on a user interaction with a “Map>”button). As shown by reference number 511, analytics device 230 mayprovide information that identifies categorized ticket information(e.g., via a “Final Mapping” portion of the user interface). As shown,the user interface may include a “Clear” button (e.g., to delete mappinginformation that incorrectly associates a ticket information categoryand an analysis category).

For the purpose of FIG. 5D, assume that analytics device 230 receivesticket information that identifies a priority level of each serviceticket, a status indicator of each service ticket (e.g., that indicatesan open status or a resolved status of each service ticket), and SLAresponse ticket data (e.g., that indicates whether each service ticketachieved an agreed-upon response time). As shown in FIG. 5D, and byreference number 512, assume that analytics device 230 receives mappinginformation related to ticket data (e.g., via a portion of the userinterface associated with a “Data Mapping” tab).

As shown by reference number 513, assume that analytics device 230receives an indication that the ticket data is SLA resolution ticketdata (e.g., based on a user interaction with a triangular buttonprovided for display in association with the name of “SLA Resolution,”or the like). As further shown, assume that analytics device 230indicates that the SLA resolution ticket data is required (e.g., byproviding for display an “x” in association with the name of “SLAResolution”). As shown, assume that analytics device 230 indicates thatanalytics device 230 received ticket information that identifies apriority level of each service ticket, a status indicator of eachservice ticket, and SLA response ticket data (e.g., by providing fordisplay a check mark in association with the names of “Priority,”“Status,” and “SLA Response”).

As shown by reference number 514, assume that analytics device 230receives mapping information that associates the ticket informationcategory with an analysis category (e.g., based on a user interactionwith the “Source Fields” portion of the user interface and a userinteraction with the “Destination Fields” portion of the userinterface). Based on the mapping information, analytics device 230 maycategorize the ticket data to create categorized ticket information(e.g., based on a user interaction with the “Map>” button).

As shown in FIG. 5E, and by reference number 515, analytics device 230may receive ticket information related to one or more priority levels(e.g., based on a user interaction with a “priority” check box, or thelike). As further shown, analytics device 230 may receive mappinginformation related to the one or more priority levels (e.g., based on auser interaction with the “Source Fields” portion of the user interfaceand a user interaction with the “Destination Fields” portion of the userinterface). As shown by reference number 516, analytics device 230 maycategorize the ticket information related to the one or more prioritylevels to create categorized ticket information.

As shown by reference number 517, analytics device 230 may provide apreliminary report that describes the categorized ticket information. Asfurther shown, analytics device 230 may provide a number of servicetickets received. As shown by reference number 518, analytics device 230may provide a list of one or more outliers (e.g., if analytics device230 determined one or more outliers, analytics device 230 may provide alist of the one or more outliers). As further shown, analytics device230 may export the categorized ticket information and/or the one or moreoutliers (e.g., to a spreadsheet or the like, and based on a userinteraction with an “export” button).

As indicated above, FIGS. 5A-5E are provided merely as an example. Otherexamples are possible and may differ from what was described with regardto FIGS. 5A-5E.

FIGS. 6A-6C are diagrams of an example implementation 600 relating toexample process 400 shown in FIG. 4. FIGS. 6A-6C show an example ofcategorizing ticket information and processing outliers. For the purposeof FIGS. 6A-6C, assume that analytics device 230 has received a batch ofticket information including 4,299 service tickets that are associatedwith a priority level of P2. Assume further that analytics device 230has determined that 866 of the service tickets are outliers based on aresolution time of each of the outliers being greater than a resolutiontime of other service tickets of the batch of ticket information. Assumethat analytics device 230 provides a preliminary report before uploadingthe batch of ticket information to server device 220.

As shown in FIG. 6A, analytics device 230 may provide, with thepreliminary report, information identifying outliers. Here, analyticsdevice 230 has provided, for each outlier, an incident number, apriority level identifier, a status identifier, a reported date, a timeof resolution, an SLA resolution indicator, and a reason each outlierwas determined. Assume that the particular outliers described withregard to FIGS. 6B and 6C, below, are associated with the same batch ofticket information as the outliers described in FIG. 6A, but may bedifferent particular outliers.

As shown in FIG. 6B, analytics device 230 may provide, in associationwith the preliminary report, a user interface, via which a user cancause user device 210 to provide an outlier action indicator. As shownby reference number 610, analytics device 230 may provide, via userdevice 210 and with the preliminary report, a list of non-outlierservice tickets and information associated with the non-outlier servicetickets. As shown by reference number 620, analytics device 230 mayfurther provide a list of outliers, and may indicate a default action(e.g., analytics device 230 may exclude outliers by default, and mayrequire user device 210 to indicate any outlier that analytics device230 is to include with the categorized ticket information). As furthershown, user device 210 may indicate an outlier to include with thecategorized ticket information (e.g., user device 210 has indicated theoutlier associated with incident number 88158028 by selecting thecheckbox provided for display next to the incident number). As shown byreference number 630, user device 210 may provide an outlier actionindicator that indicates that analytics device 230 is to include onlythe selected outlier with the categorized ticket information (e.g.,based on a user interaction with the “Merge Selected” button of the userinterface). In some implementations, user device 210 may provide anoutlier action indicator that indicates that analytics device 230 is toinclude all outliers with the categorized ticket information (e.g.,based on a user interacting with the “Merge ALL Outliers” button of theuser interface). As shown by reference number 640, user device 210 maycause analytics device 230 to prepare a preview of the categorizedticket information that includes the outlier associated with incidentnumber 88158028.

As shown in FIG. 6C, analytics device 230 may provide the preview of thecategorized ticket information that includes the outlier. As shown byreference number 650, analytics device 230 may provide an indication ofa number of outliers included with the categorized ticket informationbased on the outlier action indicator (e.g., one outlier was included,so analytics device 230 indicates that the one outlier was included).

As shown by reference number 660, analytics device 230 may provide, viaa data converter portion of the user interface, the categorized ticketinformation including the outlier. Based on a user interaction,analytics device 230 may provide the categorized ticket information toanother device, such as server device 220, as described in more detailelsewhere herein.

As indicated above, FIGS. 6A-6C are provided merely as an example. Otherexamples are possible and may differ from what was described with regardto FIGS. 6A-6C.

FIG. 7 is a flow chart of an example process 700 for analyzing ticketinformation and generating an analysis report. In some implementations,one or more process blocks of FIG. 7 may be performed by analyticsdevice 230. In some implementations, one or more process blocks of FIG.7 may be performed by another device or a set of devices separate fromor including analytics device 230, such as user device 210 and/or serverdevice 220.

As shown in FIG. 7, process 700 may include receiving an analysisrequest, requesting an analysis of ticket information (block 710). Forexample, analytics device 230 may receive an analysis request from userdevice 210. The analysis request may request that analytics device 230or another device perform an analysis of categorized ticket information.In some implementations, the categorized ticket information may bestored locally by analytics device 230. Additionally, or alternatively,the categorized ticket information may be stored by another device, suchas server device 220.

In some implementations, analytics device 230 may receive the analysisrequest based on a user interaction. For example, analytics device 230may provide, via user device 210, a user interface. A user may interactwith the user interface (e.g., with a button provided in associationwith the preliminary report) to cause analytics device 230 to receive ananalysis request. In this way, analytics device 230 may facilitate auser interaction to cause analytics device 230 to analyze categorizedticket information.

Additionally, or alternatively, analytics device 230 may receive a userinteraction with an analysis report. For example, assume that analyticsdevice 230 has previously provided an analysis report that describescategorized ticket information. Assume further that the analysis reportfacilitates a user interaction to cause analytics device 230 to furtheranalyze the categorized ticket information (e.g., assume that a user mayclick on a particular data point in a chart to cause analytics device230 to further analyze the particular data point). Based on the userinteraction with the analysis report, analytics device 230 may receivean analysis request. In this way, analytics device 230 may receive ananalysis request based on a user interaction with a previously providedanalysis report.

In some implementations, analytics device 230 may receive an analysisrequest that requests that analytics device 230 perform a predefinedtype of analysis. For example, assume that analytics device 230 isconfigured to determine turnaround times of a batch of service ticketsbased on categorized ticket information associated with the batch ofservice tickets. Assume further that analytics device 230 provides, viauser device 210, a user interface that facilitates an analysis requestto determine the turnaround times associated with a batch of servicetickets. Based on a user interaction with the user interface, analyticsdevice 230 may receive the analysis request. In this way, analyticsdevice 230 may aid a user in requesting a commonly-performed type ofanalysis, by pre-defining the commonly-performed type of analysis.

In some implementations, analytics device 230 may receive an analysisrequest that requests a user-defined type of analysis. For example,assume that analysis device 230 receives ticket information thatincludes a user-defined category associated with geographical regionticket data. Assume further that a user, via user device 210, providesan analysis request to cause analytics device 230 to analyze categorizedticket information based on the user-defined category (e.g., byselecting a “geographical region” option in a user interface thatfacilitates analysis requests). Analytics device 230 may receive theanalysis request, and may analyze the categorized ticket informationbased on the user-defined category (e.g., may calculate one or moreturnaround times of tickets associated with a particular geographicalregion, or the like). In this way, analytics device 230 may facilitateanalysis based on a user-defined category, which may allow a user toanalyze information unique to a particular client, project, application,or the like.

In some implementations, analytics device 230 may request categorizedticket information based on the analysis request. For example, assumethat analytics device 230 receives an analysis request that requestsanalysis of categorized ticket information that is stored by serverdevice 220 (e.g., analytics device 230 may have previously provided thecategorized ticket information to server device 220, and may not locallystore the categorized ticket information). Based on the analysisrequest, analytics device 230 may request, from server device 220, thecategorized ticket information. In this way, analytics device 230 mayreduce local storage requirements by storing categorized ticketinformation on server device 220.

As shown in FIG. 7, process 700 may include receiving categorized ticketinformation from a server device based on the analysis request (block720). For example, analytics device 230 may receive categorized ticketinformation from server device 220 based on the analysis request (e.g.,if the analysis request requested analysis of categorized ticketinformation that is not locally stored by analysis device 230). In someimplementations, analytics device 230 may request the categorized ticketinformation from server device 220 based on an analysis request.

In some implementations, analytics device 230 may receive a portion ofavailable categorized ticket information. For example, assume thatserver device 220 stores categorized ticket information related to athree-year range of tickets. Assume further that analytics device 230receives an analysis request to analyze only categorized ticketinformation related to a second year of the three-year range. Based onthe analysis request, analytics device 230 may receive categorizedticket information relating to only the second year of the three-yearrange. In this way, analytics device 230 may reduce network, storage,and processor requirements by receiving a portion of availablecategorized ticket information.

As further shown in FIG. 7, process 700 may include analyzing thecategorized ticket information based on the analysis request (block730). For example, analytics device 230 may analyze the categorizedticket information based on the analysis request. In someimplementations, another device, such as server device 220, may analyzethe categorized ticket information. In that case, server device 220 mayprovide analysis information to analytics device 230.

In some implementations, analytics device 230 may analyze an outlier ofthe categorized ticket information. For example, assume that analyticsdevice 230 receives an analysis request that requests that analyticsdevice 230 analyze categorized ticket information that includes anoutlier (e.g., the analysis request may request that analytics device230 generate a box plot of the categorized ticket information thatincludes the outlier). Based on the analysis request, analytics device230 may include the outlier when analyzing the categorized ticketinformation. Additionally, or alternatively, analytics device 230 mayexclude the outlier when analyzing the categorized ticket information.For example, analytics device 230 may receive a user interaction (e.g.,via a user interface of user device 210) to cause analytics device 230to exclude the outlier when analyzing the categorized ticketinformation. Based on receiving the user interaction, analytics device230 may exclude the outlier. In this way, analytics device 230 mayselectively include or exclude an outlier, to aid the user ininterpreting the categorized ticket information and understanding theanalysis report.

In some implementations, analytics device 230 may analyze thecategorized ticket information based on a stratification parameter. Forexample, assume that analytics device 230 receives an analysis requestto analyze categorized ticket information based on a stratificationparameter (e.g., a priority level of a batch of categorized ticketinformation). Analytics device 230 may analyze the batch of categorizedticket information based on the stratification parameter, and maygenerate an analysis report based on the stratification parameter (e.g.,a chart, showing categorized ticket information related to each prioritylevel). In this way, analytics device 230 may analyze categorized ticketinformation and may generate an analysis report based on astratification parameter, to aid the user in interpreting thecategorized ticket information to analyze a metric across multiple,different stratification levels.

In some implementations, analytics device 230 may analyze thecategorized ticket information based on a predefined type of analysis.For example, assume that analytics device 230 is configured to determinea resolution turnaround time (e.g., an amount of time passed betweenreceiving a particular service ticket and resolving the particularservice ticket). Analytics device 230 may analyze the categorized ticketinformation to determine the resolution turnaround time.

Additionally, or alternatively, analytics device 230 may analyze thecategorized ticket information based on a user-defined type of analysis.For example, assume that user device 210 provides a rule to configureanalytics device 230 to determine an average effort level based on ageographic location associated with a batch of service tickets (e.g.,user device 210 may provide the rule based on a user interaction, or thelike). Based on the rule, analytics device 230 may determine the averageeffort level based on the geographic location associated with the batchof service tickets. In this way, analytics device 230 may be configuredto perform a user-defined type of analysis, which may aid the user ininterpreting categorized ticket information that includes a user-definedcategory and improve user analysis of the categorized ticketinformation.

As further shown in FIG. 7, process 700 may include generating and/orproviding an analysis report based on analyzing the categorized ticketinformation (block 740). For example, analytics device 230 may generatean analysis report based on analyzing the categorized ticketinformation. In some implementations, the analysis report may include agraphical representation of categorized ticket information, a graphicalrepresentation of a stratification parameter, multiple, differentgraphical representations of categorized ticket information and/or astratification parameter, textual information that describes categorizedticket information, an element that supports user interaction, or thelike.

In some implementations, another device, such as server device 220, mayprovide analysis information used to generate the analysis report. Forexample, assume that analytics device 230 receives an analysis requestto analyze categorized ticket information. Assume further that serverdevice 220 stores the categorized ticket information. Rather thanrequesting the categorized ticket information from server device 220,analytics device 230 may request analysis information based on thecategorized ticket information from server device 220. Server device 220may analyze the categorized ticket information, and may provide theanalysis information to analytics device 230. Based on the analysisinformation, analytics device 230 may generate the analysis report. Inthis way, analysis device 230 may reduce local processing and storagerequirements, by receiving analysis information from server device 220rather than analyzing the categorized ticket information locally.

In some implementations, the analysis report may facilitate a userinteraction. For example, assume that an analysis report includes a bargraph that shows an average turnaround time of a batch of tickets ineach year of a four year time period. In other words, the bar graph mayinclude four bars. In some implementations, user device 210 may causeanalytics device 230 to provide for display a different time period(e.g., a three year time period; a one year time period, with the bargraph displaying an average turnaround time of the batch of servicetickets for each quarter, each month, each day, or the like, of the oneyear time period; or the like). In some implementations, user device 210may cause analytics device 230 to display information related to theanalysis report. For example, given the bar graph described above, userdevice 210 may cause analytics device 230 to display more informationrelated to an average turnaround time (e.g., based on a user clicking onthe average turnaround time shown), such as a number of tickets includedin the average turnaround time calculation, a standard deviation of theaverage turnaround time, information related to an outlier included inthe average turnaround time, or the like.

In some implementations, an interaction with the analysis report maycause analytics device 230 to provide for display information at ahigher level of detail. For example, assume that analytics device 210provides, for display in an analysis report, a chart that showsinformation related to four clients. Assume further that a userinteracts with the chart by clicking a name of a particular client.Based on the user interaction, analytics device 230 may provide fordisplay LOB information that describes one or more lines of business ofthe particular client. Assume that the user further interacts with theLOB information (e.g., by selecting one of the lines of business). Inthat case, analytics device 230 may provide for display applicationinformation related to the line of business that the user selected. Inthis way, analytics device 230 may aid a user in drilling down into moredetailed information that is provided by an analysis report, which mayaid the user in determining an improvement opportunity related to theanalysis information.

In some implementations, the analysis report may support filtering. Forexample, assume that an analysis report includes categorized ticketinformation that is associated with a variety of geographical origins.Assume further that the analysis report lists the variety ofgeographical origins and facilitates a user interaction to filter theanalysis report based on the variety of geographical origins (e.g., bylisting, individually, each of the variety of geographical origins, andassociating a checkbox or the like with each of the variety ofgeographical origins). Assume that user device 210 causes analyticsdevice 230 to filter the analysis report based on the variety ofgeographical origins (e.g., based on a user interaction of selecting asubset of the variety of geographical origins). Based on the userinteraction, analytics device 230 may filter the analysis report (e.g.,may display only information associated with the selected subset of thevariety of geographical origins, may highlight information associatedwith the selected subset of the variety of geographical origins, or thelike). In this way, analytics device 230 may aid a user in understandingthe analysis report by filtering the information included in theanalysis report.

Although FIG. 7 shows example blocks of process 700, in someimplementations, process 700 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 7. Additionally, or alternatively, two or more of theblocks of process 700 may be performed in parallel.

FIGS. 8A-8D are diagrams of an example implementation 800 relating toexample process 700 shown in FIG. 7. FIGS. 8A-8D show an example ofanalyzing ticket information and generating an analysis report.

As shown in FIG. 8A, analytics device 230 may provide a user interfacefor generating and providing an analysis report. As shown by referencenumber 805, the user interface may be associated with a “generatereport” tab. As shown by reference number 810, analytics device 230 mayreceive information that defines a metric (e.g., a response turnaroundtime metric, a resolution turnaround time metric, a SLA resolutionmetric, a ticket inflow rate metric, an effort data metric, an effortper FTE unit metric, a total effort metric, a backlog processingefficiency metric, an aging metric, or the like), a chart type (e.g., abox plot, a scatter plot, a pie graph, a histogram, or the like), andduration information (e.g., a monthly option to display information on amonthly basis, a quarterly option to display information on a quarterlybasis, and one or more dates to define a range of the analysis report).

As shown by reference number 815, analytics device 230 may receive astratification parameter (e.g., via the user interface for generatingand providing the analysis report). Here, analytics device 230 mayreceive a stratification parameter related to a geographical location, atechnology, a ticket category, an operating group, a priority level, aclient identifier, a project identifier, and/or a team size range. Asshown by reference number 820, analytics device 230 may receive anindicator to perform a comparison of the categorized ticket information.For example, analytics device 230 may perform a comparison of thecategorized ticket information based on a geographical location and/oron a technology type. In some implementations, analytics device 230 maygenerate a graphical representation based on performing the comparison,and may provide the graphical representation for display. Here, assumethat analytics device 230 has not received the indicator to perform thecomparison of the categorized ticket information.

As shown by reference number 825, analytics device 230 may receiveinformation related to display options. For example, analytics device230 may receive an indicator to display ticket data in association withthe analysis report, an indicator to display a legend in associationwith the analysis report, and/or information related to a chart todisplay in association with the analysis report (e.g., a base unit ofthe chart, a scale of a y-axis of the chart, a range of the y-axis ofthe chart, and/or a range of an x-axis of the chart).

For the purpose of FIG. 8B, assume that analytics device 230 receives anindicator to generate the analysis report. As shown in FIG. 8B, assumethat analytics device 230 generates the analysis report based onreceiving the indicator to generate the analysis report. Assume furtherthat analytics device 230 generates a chart based on a ticket inflowrate (e.g., a number of tickets received per month) and a stratificationparameter (e.g., a particular priority level of service tickets, or thelike). As shown by reference number 830, analytics device 230 mayprovide, for display, a chart. As shown, the chart may describecategorized ticket information based on the stratification parameter. Asfurther shown, analytics device 230 may be capable of providing theanalysis report in a variety of file formats (e.g., an extensible markuplanguage (XML) file, a comma-separated value (CSV) file, a portabledocument format (PDF) file, and so on).

For the purpose of FIGS. 8C and 8D, assume that analytics device 230provides an analysis report based on categorized ticket information thatincludes effort data (e.g., in full-time equivalent units) related tofour clients (e.g., Client1, Client2, Client3, and Client4) and threepriority levels (e.g., shown as P1, P2, and P3) that are associated withservice tickets from the four clients. As shown in FIG. 8C, and byreference number 835, assume that analytics device 230 provides a bargraph that describes the effort data and the three priority levels. Asshown by reference number 840, analytics device 230 may provideinformation related to the bar graph (e.g., a number of service ticketsassociated with effort data, a number of clients associated with effortdata, and a number of projects associated with effort data).

As shown by reference number 845, analytics device 230 may provide oneor more filters (e.g., an application name filter, a change in effortper service ticket filter, or the like). If analytics device 230receives an indicator to apply the one or more filters, analytics device230 may apply the one or more filters to the categorized ticketinformation, and may generate an analysis report based on applying theone or more filters. For example, if analytics device 230 receives anindicator to apply the application name filter (e.g., based on a userinteraction with the user interface), analytics device 230 may generatea bar graph that describes effort data based on an application name. Inthis way, analytics device 230 may generate analytic information basedon a variety of stratification parameters and/or ticket fields.

As shown by reference number 850, assume that analytics device 230receives a user interaction with an element of the analysis report(e.g., with a “P3” element of the legend associated with the bar graph).Assume further that analytics device 230 generates a bar graph ofinformation related to service tickets associated with a priority levelof P3 based on the user interaction with the “P3” element of the legend.

As shown in FIG. 8D, and by reference number 855, analytics device 230may provide for display a bar graph of information related to servicetickets associated with a priority level of P3 (e.g., based on the userinteraction described in FIG. 8C). As shown by reference number 860,analytics device 230 may provide additional information based on theuser interaction with the “P3” element of the legend (e.g., a number ofservice tickets analyzed to create the analysis report).

As indicated above, FIGS. 8A-8D are provided merely as an example. Otherexamples are possible and may differ from what was described with regardto FIGS. 8A-8D.

As described herein, analytics device 230 may receive ticket informationand may categorize the ticket information to create categorized ticketinformation. The categorized ticket information may be stored byanalytics device 230 or another device, such as server device 220. Bycreating the categorized ticket information, analytics device 230 mayimprove an efficiency of storing and/or analyzing ticket information.Analytics device 230 may further analyze the categorized ticketinformation based on predetermined or user-defined analysis types.Analytics device 230 may provide an analysis report that facilitatesuser interaction. Based on receiving a user interaction with theanalysis report, analytics device 230 may further analyze thecategorized ticket information and/or may provide for display otherinformation related to the categorized ticket information. In this way,analytics device 230 may aid a user in understanding and/or analyzingticket information; identifying an opportunity for improving a metricrelated to ticket resolution; and monitoring a performance metric ofmultiple, different teams or shifts related to multiple, differentticket information categories.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations are possible inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term component is intended to be broadly construedas hardware, firmware, and/or a combination of hardware and software.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may refer to a value beinggreater than the threshold, more than the threshold, higher than thethreshold, greater than or equal to the threshold, less than thethreshold, fewer than the threshold, lower than the threshold, less thanor equal to the threshold, equal to the threshold, etc.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, etc. A userinterface may provide information for display. In some implementations,a user may interact with the information, such as by providing input viaan input component of a device that provides the user interface fordisplay. In some implementations, a user interface may be configurableby a device and/or a user (e.g., a user may change the size of the userinterface, information provided via the user interface, a position ofinformation provided via the user interface, etc.). Additionally, oralternatively, a user interface may be pre-configured to a standardconfiguration, a specific configuration based on a type of device onwhich the user interface is displayed, and/or a set of configurationsbased on capabilities and/or specifications associated with a device onwhich the user interface is displayed.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of possible implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related itemsand unrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A device, comprising: one or more processors to:receive ticket information associated with one or more service tickets,the ticket information including a ticket category and ticket dataassociated with the one or more service tickets, the ticket data beingassociated with the ticket category, and the ticket data describinginformation related to resolving the one or more service tickets;identify, based on the ticket category or the ticket data, anassociation between the ticket category and an analysis category, theanalysis category being used to analyze the ticket information; generatecategorized ticket information based on the analysis category and theticket information, the categorized ticket information including theticket data, the ticket data being associated with the analysiscategory; and provide the categorized ticket information.
 2. The deviceof claim 1, where the one or more processors are further to: determinean outlier of the categorized ticket information based on the ticketdata; provide a report that includes information identifying theoutlier, the report requesting an outlier action indicator, the outlieraction indicator indicating an action for the device to perform relatedto the outlier; receive the outlier action indicator; and where the oneor more processors, when providing the categorized ticket information,are further to: selectively remove the outlier or include the outlierwith the categorized ticket information based on the outlier actionindicator.
 3. The device of claim 1, where the one or more processorsare further to: receive an analysis request, the analysis requestrequesting an analysis of the categorized ticket information; performthe analysis of the categorized ticket information, based on receivingthe analysis request, to determine analysis information; generate ananalysis report based on the analysis information; and provide theanalysis report.
 4. The device of claim 3, where the categorized ticketinformation is first categorized ticket information; and where the oneor more processors, when generating the categorized ticket information,are further to: receive, from a server device, second categorized ticketinformation, the second categorized ticket information being receivedbased on the analysis request; and where the one or more processors,when performing the analysis of the categorized ticket information, arefurther to: perform the analysis of the first categorized ticketinformation and the second categorized ticket information to determinethe analysis information.
 5. The device of claim 1, where the ticketcategory is a user-defined ticket category; and where the one or moreprocessors, when identifying the analysis category, are further to:identify a user-defined analysis category associated with theuser-defined ticket category.
 6. The device of claim 1, where the one ormore processors are further to: receive a stratification parameterdefining a metric for generating analysis information; determineanalysis information based on the stratification parameter and thecategorized ticket information; and provide the analysis information. 7.The device of claim 1, where the one or more processors, when providingthe categorized ticket information, are further to: receive an uploadindicator, the upload indicator indicating whether to provide thecategorized ticket information to a server device; and selectivelyprovide the categorized ticket information to the server device based onthe upload indicator, the categorized ticket information being providedto the server device when the upload indicator indicates to provide thecategorized ticket information, and the categorized ticket informationnot being provided to the server device when the upload indicator doesnot indicate to provide the categorized ticket information.
 8. Acomputer-readable medium storing instructions, the instructionscomprising: one or more instructions that, when executed by one or moreprocessors of a device, cause the one or more processors to: receiveticket information associated with one or more service tickets, theticket information including a ticket category and ticket dataassociated with the one or more service tickets, the ticket categorybeing associated with the ticket data, and the ticket data describinginformation related to resolving the one or more service tickets;identify, based on the ticket category or the ticket data, an analysiscategory associated with the ticket category; generate categorizedticket information based on the analysis category and the ticketinformation, the categorized ticket information including the ticketdata, the ticket data being associated with the analysis category;analyze the categorized ticket information to determine analysisinformation; generate an analysis report that describes the analysisinformation; and provide, for display, the analysis report.
 9. Thecomputer-readable medium of claim 8, where the one or more instructions,when executed by the one or more processors, further cause the one ormore processors to: determine, via a user interface, an interaction withan element of the analysis report; provide, based on the interaction, amodified analysis report, the modified analysis report includingadditional information related to the element.
 10. The computer-readablemedium of claim 8, where the one or more instructions, when executed bythe one or more processors, further cause the one or more processors to:determine an outlier of the categorized ticket information based on theticket data; provide an outlier report that includes informationidentifying the outlier, the outlier report requesting an outlier actionindicator, the outlier action indicator indicating an action for the oneor more processors to perform related to the outlier; receive theoutlier action indicator; and selectively remove the outlier or includethe outlier with the categorized ticket information based on the actionindicated by the outlier action indicator.
 11. The computer-readablemedium of claim 10, where the one or more instructions, that cause theone or more processors to provide the outlier report, further cause theone or more processors to: provide a preliminary report based on thecategorized ticket information, the preliminary report being provided inassociation with the outlier report, and the preliminary reportdescribing the categorized ticket information.
 12. The computer-readablemedium of claim 8, where the one or more instructions further cause theone or more processors to: receive a stratification parameter defining ametric for determining analysis information; and where the one or moreinstructions, that cause the one or more processors to determine theanalysis information, further cause the one or more processors to:determine the analysis information based on the stratification parameterand the categorized ticket information.
 13. The computer-readable mediumof claim 8, where the ticket category is a user-defined ticket category;and where the one or more instructions, that cause the one or moreprocessors to identify the analysis category, further cause the one ormore processors to: identify a user-defined analysis category associatedwith the user-defined ticket category.
 14. The computer-readable mediumof claim 8, where the categorized ticket information is firstcategorized ticket information; and where the one or more instructions,that cause the one or more processors to generate the categorized ticketinformation, further cause the one or more processors to: receive, froma server device, second categorized ticket information; and where theone or more instructions, that cause the one or more processors toanalyze the categorized ticket information, further cause the one ormore processors to: analyze the first categorized ticket information andthe second categorized ticket information to determine the analysisinformation.
 15. A method, comprising: receiving, by a device, ticketinformation associated with one or more service tickets, the ticketinformation including a ticket category and ticket data associated withthe one or more service tickets, the ticket data being associated withthe ticket category, and the ticket data describing information relatedto resolving the one or more service tickets; identifying, by the deviceand based on the ticket category or the ticket data, an associationbetween the ticket category and an analysis category, the analysiscategory being used to analyze the ticket information; generating, bythe device, categorized ticket information based on the analysiscategory and the ticket information, the categorized ticket informationincluding the ticket data, the ticket data being associated with theanalysis category; determining, by the device and based on the ticketdata, an outlier of the categorized ticket information; providing, bythe device, an outlier report that includes information identifying theoutlier, the outlier report requesting an outlier action indicator, theoutlier action indicator indicating an action for the device to performrelated to the outlier; selectively removing or including, by the deviceand based on the action indicated by the outlier action indicator, theoutlier with the categorized ticket information; and providing, by thedevice, the categorized ticket information.
 16. The method of claim 15,further comprising: receiving an analysis request, the analysis requestrequesting an analysis of the categorized ticket information; analyzingthe categorized ticket information, based on the analysis request, todetermine analysis information; generating an analysis report based onthe analysis information; and providing the analysis report.
 17. Themethod of claim 16, further comprising: receiving a stratificationparameter defining a metric for generating analysis information; andwhere analyzing the categorized ticket information further comprises:analyzing the categorized ticket information based on the stratificationparameter and the categorized ticket information to determine theanalysis information.
 18. The method of claim 16, where generating theanalysis report further comprises: generating a graphical representationof the analysis information, the graphical representation facilitating auser interaction; and providing, with the analysis report, the graphicalrepresentation of the analysis information.
 19. The method of claim 15,where providing the categorized ticket information further comprises:receiving an upload indicator, the upload indicator indicating whetherto provide the categorized ticket information to a server device; andselectively providing the categorized ticket information to the serverdevice based on the upload indicator, the categorized ticket informationbeing provided to the server device when the upload indicator indicatesto provide the categorized ticket information, and the categorizedticket information not being provided to the server device when theupload indicator does not indicate to provide the categorized ticketinformation.
 20. The method of claim 15, where the ticket category is auser-defined ticket category; and where identifying the analysiscategory further comprises: identifying a user-defined analysis categoryassociated with the user-defined ticket category.